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Jeong MS, Mun JY, Yang GE, Kim MH, Lee SY, Choi YH, Kim HS, Nam JK, Kim TN, Leem SH. Exploring the Relationship between CLPTM1L-MS2 Variants and Susceptibility to Bladder Cancer. Genes (Basel) 2023; 15:50. [PMID: 38254939 PMCID: PMC10815179 DOI: 10.3390/genes15010050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/22/2023] [Accepted: 12/26/2023] [Indexed: 01/24/2024] Open
Abstract
CLPTM1L (Cleft Lip and Palate Transmembrane Protein 1-Like) has previously been implicated in tumorigenesis and drug resistance in cancer. However, the genetic link between CLPTM1L and bladder cancer remains uncertain. In this study, we investigated the genetic association of variable number of tandem repeats (VNTR; minisatellites, MS) regions within CLPTM1L with bladder cancer. We identified four CLPTM1L-MS regions (MS1~MS4) located in intron regions. To evaluate the VNTR polymorphic alleles, we analyzed 441 cancer-free controls and 181 bladder cancer patients. Our analysis revealed a higher frequency of specific repeat sizes within the MS2 region in bladder cancer cases compared to controls. Notably, 25 and 27 repeats were exclusively present in the bladder cancer group. Moreover, rare alleles within the medium-length repeat range (25-29 repeats) were associated with an elevated bladder cancer risk (odds ratio [OR] = 5.78, 95% confidence interval [CI]: 1.49-22.47, p = 0.004). We confirmed that all MS regions followed Mendelian inheritance, and demonstrated that MS2 alleles increased CLPTM1L promoter activity in the UM-UC3 bladder cancer cells through a luciferase assay. Our findings propose the utility of CLPTM1L-MS regions as DNA typing markers, particularly highlighting the potential of middle-length rare alleles within CLPTM1L-MS2 as predictive markers for bladder cancer risk.
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Affiliation(s)
- Mi-So Jeong
- Department of Biomedical Sciences, Dong-A University, Busan 49315, Republic of Korea; (M.-S.J.); (J.-Y.M.); (G.-E.Y.); (M.-H.K.)
- Research Center, Dongnam Institute of Radiological & Medical Sciences (DIRAMS), Busan 46033, Republic of Korea
| | - Jeong-Yeon Mun
- Department of Biomedical Sciences, Dong-A University, Busan 49315, Republic of Korea; (M.-S.J.); (J.-Y.M.); (G.-E.Y.); (M.-H.K.)
| | - Gi-Eun Yang
- Department of Biomedical Sciences, Dong-A University, Busan 49315, Republic of Korea; (M.-S.J.); (J.-Y.M.); (G.-E.Y.); (M.-H.K.)
- Department of Health Sciences, The Graduated of Dong-A University, Busan 49315, Republic of Korea
| | - Min-Hye Kim
- Department of Biomedical Sciences, Dong-A University, Busan 49315, Republic of Korea; (M.-S.J.); (J.-Y.M.); (G.-E.Y.); (M.-H.K.)
| | - Sang-Yeop Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Ochang 28119, Republic of Korea;
| | - Yung Hyun Choi
- Department of Biochemistry, College of Oriental Medicine, Anti-Aging Research Center, Dong-eui University, Busan 47227, Republic of Korea;
| | - Heui Soo Kim
- Department of Biological Sciences, College of Natural Sciences, Pusan National University, Busan 46241, Republic of Korea;
| | - Jong-Kil Nam
- Department of Urology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Research Institute for Convergence of Biomedical Science and Technology, Yangsan 50612, Republic of Korea;
| | - Tae Nam Kim
- Department of Urology, Pusan National University Hospital, Pusan National University School of Medicine, Biomedical Research Institute and Pusan National University Hospital, Busan 49241, Republic of Korea
| | - Sun-Hee Leem
- Department of Biomedical Sciences, Dong-A University, Busan 49315, Republic of Korea; (M.-S.J.); (J.-Y.M.); (G.-E.Y.); (M.-H.K.)
- Department of Health Sciences, The Graduated of Dong-A University, Busan 49315, Republic of Korea
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Lu R, Tang P, Zhang D, Lin S, Li H, Feng X, Sun M, Zhang H. SOX9/NFIA promotes human ovarian cancer metastasis through the Wnt/β-catenin signaling pathway. Pathol Res Pract 2023; 248:154602. [PMID: 37315400 DOI: 10.1016/j.prp.2023.154602] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 05/03/2023] [Accepted: 06/06/2023] [Indexed: 06/16/2023]
Abstract
To our knowledge, Sex-determining Region Y box 9 (SOX9) has been in connection with a wide range of human cancers. Nevertheless, there remains uncertainty regarding SOX9's role in metastasizing ovarian cancer. In our study, SOX9 was investigated in relation to tumor metastasis in ovarian cancer as well as its potential molecular mechanisms. First, we exhibited an apparent higher expression of SOX9 in ovarian cancer tissues and cells than in normative ones, and the prognosis of patients whose SOX9 levels were high was markedly lower than that of patients whose SOX9 levels were low. Besides, highly expressed SOX9 was correlated with high grade serous carcinoma, poor tumor differentiation, high serum CA125 and lymph node metastasis. Second, SOX9 knockdown exhibited striking inhibition of the migration and invasive ability of ovarian cancer cells, whereas SOX9 overexpression had an inverse role. At the same time, SOX9 could promote ovarian cancer intraperitoneal metastasis in a nude mice in the vivo. In a similar way, SOX9 knockdown dramatically decreased the expression of nuclear factor I-A (NFIA), β-catenin as well as N-cadherin but had an increased in E-cadherin expression, as opposed to the results when SOX9 was overexpressed. Furthermore, NFIA silencing inhibited the expression of NFIA, β-catenin and N-cadherin, in the same way that E-cadherin expression was promoted. In conclusion, this study shows that SOX9 has a promotional effect on human ovarian cancer and that SOX9 promotes the metastasis of tumors by upregulating NFIA and activating on a Wnt/β-catenin signal pathway. SOX9 could be a novel focus for earlier diagnosis, therapy and prospective evaluation in ovarian cancer.
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Affiliation(s)
- Rong Lu
- Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Soochow University, Suzhou 215004 Jiangsu Province, China; Department of Gynecology and Obstetrics, The Affiliated Huai'an Hospital of Xuzhou Medical University and The Second People's Hospital of Huai'an, No.60, Huaihai Road (S.), Huai'an 223002 Jiangsu Province, China
| | - Peipei Tang
- Institute of Medicinal Biotechnology, Jiangsu College of Nursing, Huai'an 223003 Jiangsu Province, China
| | - Di Zhang
- Institute of Medicinal Biotechnology, Jiangsu College of Nursing, Huai'an 223003 Jiangsu Province, China
| | - Sen Lin
- Department of Clinical Laboratory, The Affiliated Huai'an Hospital of Xuzhou Medical University and The Second People's Hospital of Huai'an, No.60, Huaihai Road (S.), Huai'an 223002 Jiangsu Province, China
| | - Hong Li
- Department of Pathology, The Affiliated Huai'an Hospital of Xuzhou Medical University and The Second People's Hospital of Huai'an, No.60, Huaihai Road (S.), Huai'an 223002 Jiangsu Province, China
| | - Xian Feng
- Department of Pathology, The Affiliated Huai'an Hospital of Xuzhou Medical University and The Second People's Hospital of Huai'an, No.60, Huaihai Road (S.), Huai'an 223002 Jiangsu Province, China
| | - Meiling Sun
- Department of Gynecology and Obstetrics, The Affiliated Huai'an Hospital of Xuzhou Medical University and The Second People's Hospital of Huai'an, No.60, Huaihai Road (S.), Huai'an 223002 Jiangsu Province, China
| | - Hong Zhang
- Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Soochow University, Suzhou 215004 Jiangsu Province, China.
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Regularized Weighted Nonparametric Likelihood Approach for High-Dimension Sparse Subdistribution Hazards Model for Competing Risk Data. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5169052. [PMID: 34589136 PMCID: PMC8476266 DOI: 10.1155/2021/5169052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 08/09/2021] [Accepted: 08/30/2021] [Indexed: 11/18/2022]
Abstract
Variable selection and penalized regression models in high-dimension settings have become an increasingly important topic in many disciplines. For instance, omics data are generated in biomedical researches that may be associated with survival of patients and suggest insights into disease dynamics to identify patients with worse prognosis and to improve the therapy. Analysis of high-dimensional time-to-event data in the presence of competing risks requires special modeling techniques. So far, some attempts have been made to variable selection in low- and high-dimension competing risk setting using partial likelihood-based procedures. In this paper, a weighted likelihood-based penalized approach is extended for direct variable selection under the subdistribution hazards model for high-dimensional competing risk data. The proposed method which considers a larger class of semiparametric regression models for the subdistribution allows for taking into account time-varying effects and is of particular importance, because the proportional hazards assumption may not be valid in general, especially in the high-dimension setting. Also, this model relaxes from the constraint of the ability to simultaneously model multiple cumulative incidence functions using the Fine and Gray approach. The performance/effectiveness of several penalties including minimax concave penalty (MCP); adaptive LASSO and smoothly clipped absolute deviation (SCAD) as well as their L2 counterparts were investigated through simulation studies in terms of sensitivity/specificity. The results revealed that sensitivity of all penalties were comparable, but the MCP and MCP-L2 penalties outperformed the other methods in term of selecting less noninformative variables. The practical use of the model was investigated through the analysis of genomic competing risk data obtained from patients with bladder cancer and six genes of CDC20, NCF2, SMARCAD1, RTN4, ETFDH, and SON were identified using all the methods and were significantly correlated with the subdistribution.
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Mattesen TB, Andersen CL, Bramsen JB. MethCORR infers gene expression from DNA methylation and allows molecular analysis of ten common cancer types using fresh-frozen and formalin-fixed paraffin-embedded tumor samples. Clin Epigenetics 2021; 13:20. [PMID: 33509261 PMCID: PMC7842045 DOI: 10.1186/s13148-021-01000-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 01/01/2021] [Indexed: 11/10/2022] Open
Abstract
Background Transcriptional analysis is widely used to study the molecular biology of cancer and hold great biomarker potential for clinical patient stratification. Yet, accurate transcriptional profiling requires RNA of a high quality, which often cannot be retrieved from formalin-fixed, paraffin-embedded (FFPE) tumor tissue that is routinely collected and archived in clinical departments. To overcome this roadblock to clinical testing, we previously developed MethCORR, a method that infers gene expression from DNA methylation data, which is robustly retrieved from FFPE tissue. MethCORR was originally developed for colorectal cancer and with this study, we aim to: (1) extend the MethCORR method to 10 additional cancer types and (2) to illustrate that the inferred gene expression is accurate and clinically informative. Results Regression models to infer gene expression information from DNA methylation were developed for ten common cancer types using matched RNA sequencing and DNA methylation profiles (HumanMethylation450 BeadChip) from The Cancer Genome Atlas Project. Robust and accurate gene expression profiles were inferred for all cancer types: on average, the expression of 11,000 genes was modeled with good accuracy and an intra-sample correlation of R2 = 0.90 between inferred and measured gene expression was observed. Molecular pathway analysis and transcriptional subtyping were performed for breast, prostate, and lung cancer samples to illustrate the general usability of the inferred gene expression profiles: overall, a high correlation of r = 0.96 (Pearson) in pathway enrichment scores and a 76% correspondence in molecular subtype calls were observed when using measured and inferred gene expression as input. Finally, inferred expression from FFPE tissue correlated better with RNA sequencing data from matched fresh-frozen tissue than did RNA sequencing data from FFPE tissue (P < 0.0001; Wilcoxon rank-sum test). Conclusions In all cancers investigated, MethCORR enabled DNA methylation-based transcriptional analysis, thus enabling future analysis of cancer in situations where high-quality DNA, but not RNA, is available. Here, we provide the framework and resources for MethCORR modeling of ten common cancer types, thereby widely expanding the possibilities for transcriptional studies of archival FFPE material.
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Affiliation(s)
- Trine B Mattesen
- Department of Molecular Medicine, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200, Aarhus N, Denmark
| | - Claus L Andersen
- Department of Molecular Medicine, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200, Aarhus N, Denmark.
| | - Jesper B Bramsen
- Department of Molecular Medicine, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200, Aarhus N, Denmark.
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Yin S, Wang X, Jia G, Xie Y. MIXnorm: normalizing RNA-seq data from formalin-fixed paraffin-embedded samples. Bioinformatics 2020; 36:3401-3408. [PMID: 32134470 DOI: 10.1093/bioinformatics/btaa153] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 02/21/2020] [Accepted: 02/28/2020] [Indexed: 01/18/2023] Open
Abstract
MOTIVATION Recent studies have shown that RNA-sequencing (RNA-seq) can be used to measure mRNA of sufficient quality extracted from formalin-fixed paraffin-embedded (FFPE) tissues to provide whole-genome transcriptome analysis. However, little attention has been given to the normalization of FFPE RNA-seq data, a key step that adjusts for unwanted biological and technical effects that can bias the signal of interest. Existing methods, developed based on fresh-frozen or similar-type samples, may cause suboptimal performance. RESULTS We proposed a new normalization method, labeled MIXnorm, for FFPE RNA-seq data. MIXnorm relies on a two-component mixture model, which models non-expressed genes by zero-inflated Poisson distributions and models expressed genes by truncated normal distributions. To obtain maximum likelihood estimates, we developed a nested EM algorithm, in which closed-form updates are available in each iteration. By eliminating the need for numerical optimization in the M-step, the algorithm is easy to implement and computationally efficient. We evaluated MIXnorm through simulations and cancer studies. MIXnorm makes a significant improvement over commonly used methods for RNA-seq expression data. AVAILABILITY AND IMPLEMENTATION R code available at https://github.com/S-YIN/MIXnorm. CONTACT swang@smu.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shen Yin
- Department of Statistical Science, Southern Methodist University, Dallas, TX 75275-0332, USA.,Department of Population and Data Sciences, Quantitative Biomedical Research Center, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Xinlei Wang
- Department of Statistical Science, Southern Methodist University, Dallas, TX 75275-0332, USA
| | - Gaoxiang Jia
- Department of Statistical Science, Southern Methodist University, Dallas, TX 75275-0332, USA
| | - Yang Xie
- Department of Population and Data Sciences, Quantitative Biomedical Research Center, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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Afferi L, Moschini M, Cumberbatch MG, Catto JW, Scarpa RM, Porpiglia F, Mattei A, Sanchez-Salas R, Esperto F. Biomarkers predicting oncological outcomes of high-risk non-muscle-invasive bladder cancer. MINERVA UROL NEFROL 2020; 72:265-278. [PMID: 32298067 DOI: 10.23736/s0393-2249.20.03786-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION The European Organization for Research and Treatment of Cancer (EORTC) and the Spanish Urological Club for Oncological Treatment (CUETO) scoring systems show limited accuracy for the prediction of disease recurrence and progression of non-muscle-invasive bladder cancer (NMIBC). This aspect is even more relevant in the category of HR NMIBC. Biomarkers might potentially help to further categorize the outcomes of these patients. Therefore, we sought to review the evidence available on tissue-based, urinary, and serum biomarkers for the prediction of recurrence, progression, and survival in HR NMIBC. EVIDENCE ACQUISITION A systematic literature review without time restrictions was performed using PubMed/EMBASE, Web of Science, SCOPUS, and the Cochrane Libraries. The search was filtered for articles in the English, Italian, German, French, and Spanish languages, involving patients with more than 18 years of age. Relevant papers on tissue-based, serum and urinary biomarkers related to the prediction of oncological outcomes for high-risk bladder cancer patients were included in the analyses. EVIDENCE SYNTHESIS Overall, 71 studies were eligible for inclusion in this review. The majority of the investigations performed so far focused on immunohistochemical analyses on tumoral tissue. Overall, p53 was the most studied biomarker, but results regarding its prognostic and predictive role were contradictory. Ki67 seems to be a promising biomarker in the prediction of recurrence. Recently, PD-L1 has been associated with the prediction of recurrence free survival and of treatment-refractory disease. Markers developed un urine samples are focused on commercially available kits, which currently do not unequivocally show strongly superior levels of accuracy to cytology. However, they have demonstrated to be potentially helpful in the prediction of recurrence. Blood-based biomarkers represent an emerging reality with promising future applications. CONCLUSIONS Despite a long history of attempts to discover accurate biomarkers predicting oncological outcomes for HR NMIBC, contradictory or uncertain findings render the adoption of this ancillary techniques in clinical practice still unlikely. Future attempts should be directed to the development of prospective trials and the definition of standardized cut-off levels to render findings worthy of comparison.
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Affiliation(s)
- Luca Afferi
- Department of Urology, Luzerner Kantonsspital, Lucerne, Switzerland
| | - Marco Moschini
- Department of Urology, Luzerner Kantonsspital, Lucerne, Switzerland - .,Department of Urology, Urological Research Institute, IRCCS San Raffaele Hospital, Vita-Salute San Raffaele University, Milan, Italy
| | | | - James W Catto
- Unit of Academic Urology, University of Sheffield, Sheffield, UK
| | - Roberto M Scarpa
- Department of Urology, Campus Bio-Medico University, Rome, Italy
| | - Francesco Porpiglia
- Division of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Turin, Italy
| | - Agostino Mattei
- Department of Urology, Luzerner Kantonsspital, Lucerne, Switzerland
| | - Rafael Sanchez-Salas
- Department of Urology, Institut Mutualiste Montsouris and Universitè Paris Descartes, Paris, France
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Li Y, Sun C, Tan Y, Li L, Zhang H, Liang Y, Zeng J, Zou H. Transcription levels and prognostic significance of the NFI family members in human cancers. PeerJ 2020; 8:e8816. [PMID: 32219034 PMCID: PMC7085295 DOI: 10.7717/peerj.8816] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 02/27/2020] [Indexed: 12/24/2022] Open
Abstract
Background The nuclear factor I (NFI) is a family of transcription factors consisting of four distinct but closely related genes, NFIA, NFIB, NFIC and NFIX, which are important in the development of various tissues and organs in mammals. Recent study results have shown that NFI family may play a critical role in the progression of various human tumors and have been identified as key tumor suppressors and oncogenes for many cancers. However, the expression levels and distinctive prognostic values of the NFI family remain poorly explored in most cancers. Materials and Methods In the present study, the differences in mRNA expression of the NFI family in various cancers were investigated using the Oncomine and TCGA databases, and the mRNA expression, genetic alteration and DNA methylation of the NFI family members in various cancers were examined using cBioPortal for Cancer Genomics. In addition, the prognostic significance of the NFI family was assessed in multiple cancers using the Kaplan–Meier plotter (KM plotter) and SurvExpress databases. Results The mRNA expression levels in the NFI family were significantly downregulated in most cancers compared with normal tissues and DNA hypermethylation might downregulate the NFI family expression. Although NFIX expression was not downregulated in kidney, colorectal and prostate cancers. Furthermore, NFIB expression was upregulated in gastric cancer. Further survival analyses based on the KM plotter and SurvExpress databases showed dysregulations of the NFI genes were significantly correlated with survival outcomes in breast, lung, and head and neck cancers. Decreased expression levels of NFIA, NFIB and NFIC were associated with poor overall survival (OS) in head and neck cancer. Low mRNA expression of NFIA and NFIB was significantly associated with OS and first progression in lung adenocarcinoma, but not in lung squamous cell carcinoma. In addition, potential correlations between NFI family members and survival outcomes were also observed in liver, esophageal, kidney and cervical cancer. Conclusion The results from the present study indicated certain members of the NFI family could be promising therapeutic targets and novel prognostic biomarkers for human cancers.
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Affiliation(s)
- Yuexian Li
- The First Oncology Department, Shengjing Hospital affiliated with China Medical University, Shenyang, China
| | - Cheng Sun
- The First Oncology Department, Shengjing Hospital affiliated with China Medical University, Shenyang, China
| | - Yonggang Tan
- The First Oncology Department, Shengjing Hospital affiliated with China Medical University, Shenyang, China
| | - Lin Li
- The First Oncology Department, The Fourth Hospital affiliated with China Medical University, Shenyang, China
| | - Heying Zhang
- The First Oncology Department, Shengjing Hospital affiliated with China Medical University, Shenyang, China
| | - Yusi Liang
- The First Oncology Department, Shengjing Hospital affiliated with China Medical University, Shenyang, China
| | - Juan Zeng
- The First Oncology Department, Shengjing Hospital affiliated with China Medical University, Shenyang, China
| | - Huawei Zou
- The First Oncology Department, Shengjing Hospital affiliated with China Medical University, Shenyang, China
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Pennock ND, Jindal S, Horton W, Sun D, Narasimhan J, Carbone L, Fei SS, Searles R, Harrington CA, Burchard J, Weinmann S, Schedin P, Xia Z. RNA-seq from archival FFPE breast cancer samples: molecular pathway fidelity and novel discovery. BMC Med Genomics 2019; 12:195. [PMID: 31856832 PMCID: PMC6924022 DOI: 10.1186/s12920-019-0643-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 12/08/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Formalin-fixed, paraffin-embedded (FFPE) tissues for RNA-seq have advantages over fresh frozen tissue including abundance and availability, connection to rich clinical data, and association with patient outcomes. However, FFPE-derived RNA is highly degraded and chemically modified, which impacts its utility as a faithful source for biological inquiry. METHODS True archival FFPE breast cancer cases (n = 58), stored at room temperature for 2-23 years, were utilized to identify key steps in tissue selection, RNA isolation, and library choice. Gene expression fidelity was evaluated by comparing FFPE data to public data obtained from fresh tissues, and by employing single-gene, gene set and transcription network-based regulon analyses. RESULTS We report a single 10 μm section of breast tissue yields sufficient RNA for RNA-seq, and a relationship between RNA quality and block age that was not linear. We find single-gene analysis is limiting with FFPE tissues, while targeted gene set approaches effectively distinguish ER+ from ER- breast cancers. Novel utilization of regulon analysis identified the transcription factor KDM4B to associate with ER+ disease, with KDM4B regulon activity and gene expression having prognostic significance in an independent cohort of ER+ cases. CONCLUSION Our results, which outline a robust FFPE-RNA-seq pipeline for broad use, support utilizing FFPE tissues to address key questions in the breast cancer field, including the delineation between indolent and life-threatening disease, biological stratification and molecular mechanisms of treatment resistance.
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Affiliation(s)
- Nathan D Pennock
- Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University, 2720 SW Moody Ave, Portland, OR, 97201, USA
| | - Sonali Jindal
- Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University, 2720 SW Moody Ave, Portland, OR, 97201, USA
- Knight Cancer Institute, Oregon Health & Science University, 2720 SW Moody Ave, Portland, OR, 97201, USA
| | - Wesley Horton
- Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University, 2720 SW Moody Ave, Portland, OR, 97201, USA
- Computational Biology Program, Oregon Health & Science University, Portland, OR, 97201, USA
| | - Duanchen Sun
- Computational Biology Program, Oregon Health & Science University, Portland, OR, 97201, USA
| | - Jayasri Narasimhan
- Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University, 2720 SW Moody Ave, Portland, OR, 97201, USA
| | - Lucia Carbone
- Division of Genetics, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR, 97006, USA
- Department of Medicine, Knight Cardiovascular Institute, Oregon Health & Science University, 3303 SW Bond Ave, Portland, OR, 97239, USA
| | - Suzanne S Fei
- Bioinformatics & Biostatistics Core, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR, 97006, USA
| | - Robert Searles
- Integrated Genomics Laboratory, Knight Cancer Institute, Oregon Health & Science University Knight Cancer Institute, Portland, OR, 97239, USA
| | - Christina A Harrington
- Integrated Genomics Laboratory, Knight Cancer Institute, Oregon Health & Science University Knight Cancer Institute, Portland, OR, 97239, USA
| | - Julja Burchard
- Computational Biology Program, Oregon Health & Science University, Portland, OR, 97201, USA
| | - Sheila Weinmann
- Center for Health Research, Kaiser Permanente Northwest, Portland, OR, 97278, USA
| | - Pepper Schedin
- Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University, 2720 SW Moody Ave, Portland, OR, 97201, USA.
- Knight Cancer Institute, Oregon Health & Science University, 2720 SW Moody Ave, Portland, OR, 97201, USA.
- Young Women's Breast Cancer Translational Program, University of Colorado Anschutz Medical Campus, 1665 Aurora Court, USA, Aurora, CO, 80045, USA.
| | - Zheng Xia
- Computational Biology Program, Oregon Health & Science University, Portland, OR, 97201, USA.
- Department of Molecular Microbiology and Immunology Oregon Health & Science University, Portland, OR, 97273, USA.
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9
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Young FP, Ende D, Epstein RJ. Beyond BCG: the approaching era of personalised bladder-sparing therapies for non-muscle-invasive urothelial cancers. Future Oncol 2019; 15:409-420. [DOI: 10.2217/fon-2018-0565] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Progress in the management of non-muscle invasive bladder cancer has been slow. Despite longstanding use of intravesical therapies (e.g., Bacille Calmette-Guerin; BCG) to complement cystoscopic resection of high-grade lesions, many patients still develop recurrences requiring cystectomy, while others suffer side-effects of BCG without definite benefit. Many questions remain: for example, how many patients receive intravesical prophylaxis without efficacy? Which high-risk patients are best managed with early cystectomy? Could systemic therapies and/or radiotherapy extend bladder preservation times? Such questions may soon be refined by clinicopathologic non-muscle invasive bladder cancer signatures that predict sensitivity to cytotoxic, immune and targeted therapies. Hypothesis-based trials using these signatures should lead to more rational adjuvant treatments, longer bladder preservation times, and better quality of life for patients.
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Affiliation(s)
- Francis P Young
- University of New South Wales Clinical School, St Vincent's Hospital, 390 Victoria St, Darlinghurst 2010, Sydney, Australia
| | - David Ende
- Department of Urologic Surgery, St Vincent's Hospital, 390 Victoria St, Darlinghurst 2010, Sydney, Australia
| | - Richard J Epstein
- University of New South Wales Clinical School, St Vincent's Hospital, 390 Victoria St, Darlinghurst 2010, Sydney, Australia
- The Kinghorn Cancer Centre, Clinical Informatics & Research Centre, St Vincent's Hospital, 370 Victoria St, Darlinghurst 2010, Sydney, Australia
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10
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Kwong LN, De Macedo MP, Haydu L, Joon AY, Tetzlaff MT, Calderone TL, Wu CJ, Kwong MK, Roszik J, Hess KR, Davies MA, Lazar AJ, Gershenwald JE. Biological Validation of RNA Sequencing Data from Formalin-Fixed Paraffin-Embedded Primary Melanomas. JCO Precis Oncol 2018; 2018. [PMID: 31058252 DOI: 10.1200/po.17.00259] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Initiatives such as The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) have generated high-quality, multi-platform molecular data from thousands of frozen tumor samples. While these initiatives have provided invaluable insight into cancer biology, a tremendous potential resource remains largely untapped in formalin-fixed, paraffin-embedded (FFPE) samples that are more readily available, but which can present technical challenges due to crosslinking of fragile molecules such as RNA. MATERIALS AND METHODS We extracted RNA from FFPE primary melanomas and assessed two gene expression platforms -- genome-wide RNA sequencing (RNA-seq) and targeted NanoString -- for their ability to generate coherent biological signals. To do so, we generated an improved approach to quantifying gene expression pathways, in which we refine pathway scores through correlation-guided gene subsetting. We also make comparisons to the TCGA and other publicly available melanoma datasets. RESULTS Comparison of the gene expression patterns to each other, to established biological modules, and to clinical and immunohistochemical data confirmed the fidelity of biological signals from both platforms using FFPE samples to known biology. Moreover, correlations with patient outcome data were consistent with previous frozen-tissue-based studies. CONCLUSION FFPE samples from previously difficult-to-access cancer types - such as small primary melanomas - represents a valuable and previously unexploited source of analyte for RNA-seq and NanoString platforms. This work provides an important step towards the use of such platforms to unlock novel molecular underpinnings and inform future biologically-driven clinical decisions.
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Affiliation(s)
- Lawrence N Kwong
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX.,Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Mariana Petaccia De Macedo
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX.,Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX.,Department of Pathology, A.C. Camargo Cancer Center, Sao Paulo, Brazil
| | - Lauren Haydu
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Aron Y Joon
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Michael T Tetzlaff
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX.,Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Tiffany L Calderone
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.,Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Chiang-Jun Wu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Man Kam Kwong
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Jason Roszik
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Kenneth R Hess
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Michael A Davies
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX.,Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Alexander J Lazar
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX.,Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX.,Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Jeffrey E Gershenwald
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.,Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
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11
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Molecular Progression Risk Score for Prediction of Muscle Invasion in Primary T1 High-Grade Bladder Cancer. Clin Genitourin Cancer 2018; 16:274-280. [PMID: 29571585 DOI: 10.1016/j.clgc.2018.02.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Revised: 02/05/2018] [Accepted: 02/16/2018] [Indexed: 11/24/2022]
Abstract
BACKGROUND Pathologic T1 high-grade (pT1HG) bladder cancer (BC) is characterized by a high progression rate and constitutes an important clinical challenge; however, there is no consensus on the prediction of progression in pT1HG BC. The purpose of this study was to validate previously published molecular progression risk score (MoPRS) for predicting muscle-invasive disease in pT1HG BC. MATERIALS AND METHODS The expression of an 8-gene progression-related classifier identified from microarray data was analyzed by real-time PCR, and the MoPRS was calculated in 121 newly recruited patients with pT1HG BC. Progression was defined as muscle invasion or metastasis. RESULTS Overall, the disease of 28 patients (23.1%) progressed to muscle-invasive BC during the median follow-up of 63.7 (interquartile range, 17.6-96.4) months. The MoPRS was significantly higher in 1973 World Health Organization grade 3 than grade 2 tumors (P = .004). Early development of invasive BC was more prevalent in the highest quartile MoPRS group than in the lowest to 75th percentile MoPRS groups according to Kaplan-Meier analysis. Multivariate Cox regression analysis revealed that the MoPRS was an independent predictor of invasive BC, either as a continuous variable (hazard ratio, 1.624; 95% confidence interval, 1.266-2.082; P < .001) or as a categorical variable (hazard ratio, 3.089; 95% confidence interval, 1.335-7.150; P = .008). CONCLUSION The MoPRS was an independent prognostic factor for identifying patients at high risk of invasive BC in patients with pT1HG BC. This scale may help identify patients who could benefit from more aggressive therapeutic intervention such as early cystectomy.
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12
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The convergent roles of the nuclear factor I transcription factors in development and cancer. Cancer Lett 2017; 410:124-138. [PMID: 28962832 DOI: 10.1016/j.canlet.2017.09.015] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Revised: 09/11/2017] [Accepted: 09/16/2017] [Indexed: 02/07/2023]
Abstract
The nuclear factor I (NFI) transcription factors play important roles during normal development and have been associated with developmental abnormalities in humans. All four family members, NFIA, NFIB, NFIC and NFIX, have a homologous DNA binding domain and function by regulating cell proliferation and differentiation via the transcriptional control of their target genes. More recently, NFI genes have also been implicated in cancer based on genomic analyses and studies of animal models in a variety of tumours across multiple organ systems. However, the association between their functions in development and in cancer is not well described. In this review, we summarise the evidence suggesting a converging role for the NFI genes in development and cancer. Our review includes all cancer types in which the NFI genes are implicated, focusing predominantly on studies demonstrating their oncogenic or tumour-suppressive potential. We conclude by presenting the challenges impeding our understanding of NFI function in cancer biology, and demonstrate how a developmental perspective may contribute towards overcoming such hurdles.
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13
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The Utilization of Formalin Fixed-Paraffin-Embedded Specimens in High Throughput Genomic Studies. Int J Genomics 2017; 2017:1926304. [PMID: 28246590 PMCID: PMC5299160 DOI: 10.1155/2017/1926304] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Accepted: 01/09/2017] [Indexed: 01/09/2023] Open
Abstract
High throughput genomic assays empower us to study the entire human genome in short time with reasonable cost. Formalin fixed-paraffin-embedded (FFPE) tissue processing remains the most economical approach for longitudinal tissue specimen storage. Therefore, the ability to apply high throughput genomic applications to FFPE specimens can expand clinical assays and discovery. Many studies have measured the accuracy and repeatability of data generated from FFPE specimens using high throughput genomic assays. Together, these studies demonstrate feasibility and provide crucial guidance for future studies using FFPE specimens. Here, we summarize the findings of these studies and discuss the limitations of high throughput data generated from FFPE specimens across several platforms that include microarray, high throughput sequencing, and NanoString.
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14
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Bartsch G, Mitra AP, Mitra SA, Almal AA, Steven KE, Skinner DG, Fry DW, Lenehan PF, Worzel WP, Cote RJ. Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder. J Urol 2016; 195:493-8. [DOI: 10.1016/j.juro.2015.09.090] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/21/2015] [Indexed: 12/22/2022]
Affiliation(s)
- Georg Bartsch
- University of Southern California, Los Angeles, California
- Goethe University Frankfurt, Frankfurt, Germany
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15
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Tapak L, Saidijam M, Sadeghifar M, Poorolajal J, Mahjub H. Competing risks data analysis with high-dimensional covariates: an application in bladder cancer. GENOMICS PROTEOMICS & BIOINFORMATICS 2015; 13:169-76. [PMID: 25907251 PMCID: PMC4563215 DOI: 10.1016/j.gpb.2015.04.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Revised: 09/27/2014] [Accepted: 10/08/2014] [Indexed: 01/09/2023]
Abstract
Analysis of microarray data is associated with the methodological problems of high dimension and small sample size. Various methods have been used for variable selection in high-dimension and small sample size cases with a single survival endpoint. However, little effort has been directed toward addressing competing risks where there is more than one failure risks. This study compared three typical variable selection techniques including Lasso, elastic net, and likelihood-based boosting for high-dimensional time-to-event data with competing risks. The performance of these methods was evaluated via a simulation study by analyzing a real dataset related to bladder cancer patients using time-dependent receiver operator characteristic (ROC) curve and bootstrap .632+ prediction error curves. The elastic net penalization method was shown to outperform Lasso and boosting. Based on the elastic net, 33 genes out of 1381 genes related to bladder cancer were selected. By fitting to the Fine and Gray model, eight genes were highly significant (P<0.001). Among them, expression of RTN4, SON, IGF1R, SNRPE, PTGR1, PLEK, and ETFDH was associated with a decrease in survival time, whereas SMARCAD1 expression was associated with an increase in survival time. This study indicates that the elastic net has a higher capacity than the Lasso and boosting for the prediction of survival time in bladder cancer patients. Moreover, genes selected by all methods improved the predictive power of the model based on only clinical variables, indicating the value of information contained in the microarray features.
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Affiliation(s)
- Leili Tapak
- Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan 65175-4171, Iran
| | - Massoud Saidijam
- Research Center for Molecular Medicine, Department of Molecular Medicine and Genetics, School of Medicine, Hamadan University of Medical Sciences, Hamadan 651783-8695, Iran
| | - Majid Sadeghifar
- Department of Statistics, Bu-Ali Sina University, Hamadan 65175-4171, Iran
| | - Jalal Poorolajal
- Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan 65175-4171, Iran; Modeling of Noncommunicable Diseases Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan 65178-38695, Iran
| | - Hossein Mahjub
- Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan 65175-4171, Iran; Research Center for Health Sciences, School of Public Health, Hamadan University of Medical Sciences, Hamadan 65175-4171, Iran.
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16
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Oh S, Song S, Dasgupta N, Grabowski G. The analytical landscape of static and temporal dynamics in transcriptome data. Front Genet 2014; 5:35. [PMID: 24600473 PMCID: PMC3929947 DOI: 10.3389/fgene.2014.00035] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2013] [Accepted: 01/30/2014] [Indexed: 12/16/2022] Open
Abstract
Interpreting gene expression profiles often involves statistical analysis of large numbers of differentially expressed genes, isoforms, and alternative splicing events at either static or dynamic spectrums. Reduced sequencing costs have made feasible dense time-series analysis of gene expression using RNA-seq; however, statistical methods in the context of temporal RNA-seq data are poorly developed. Here we will review current methods for identifying temporal changes in gene expression using RNA-seq, which are limited to static pairwise comparisons of time points and which fail to account for temporal dependencies in gene expression patterns. We also review recently developed very few number of temporal dynamic RNA-seq specific methods. Application and development of RNA-specific temporal dynamic methods have been continuously under the development, yet, it is still in infancy. We fully cover microarray specific temporal methods and transcriptome studies in initial digital technology (e.g., SAGE) between traditional microarray and new RNA-seq.
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Affiliation(s)
- Sunghee Oh
- Division of Human Genetics, Department of Pediatrics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Seongho Song
- Department of Mathematical Sciences, McMicken College of Arts and Sciences, University of Cincinnati Cincinnati, OH, USA
| | - Nupur Dasgupta
- Division of Human Genetics, Department of Pediatrics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Gregory Grabowski
- Division of Human Genetics, Department of Pediatrics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
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